#Nvidia HGX

[exeton]

Nvidia HGX vs DGX: Key Differences in AI Supercomputing Solutions

Nvidia HGX vs DGX: What are the differences?

Nvidia is comfortably riding the AI wave. And for at least the next few years, it will likely not be dethroned as the AI hardware market leader. With its extremely popular enterprise solutions powered by the H100 and H200 “Hopper” lineup of GPUs (and now B100 and B200 “Blackwell” GPUs), Nvidia is the go-to manufacturer of high-performance computing (HPC) hardware.

Nvidia DGX is an integrated AI HPC solution targeted toward enterprise customers needing immensely powerful workstation and server solutions for deep learning, generative AI, and data analytics. Nvidia HGX is based on the same underlying GPU technology. However, HGX is a customizable enterprise solution for businesses that want more control and flexibility over their AI HPC systems. But how do these two platforms differ from each other?

Nvidia DGX: The Original Supercomputing Platform

It should surprise no one that Nvidia’s primary focus isn’t on its GeForce lineup of gaming GPUs anymore. Sure, the company enjoys the lion’s share among the best gaming GPUs, but its recent resounding success is driven by enterprise and data center offerings and AI-focused workstation GPUs.

Overview of DGX

The Nvidia DGX platform integrates up to 8 Tensor Core GPUs with Nvidia’s AI software to power accelerated computing and next-gen AI applications. It’s essentially a rack-mount chassis containing 4 or 8 GPUs connected via NVLink, high-end x86 CPUs, and a bunch of Nvidia’s high-speed networking hardware. A single DGX B200 system is capable of 72 petaFLOPS of training and 144 petaFLOPS of inference performance.

Key Features of DGX

AI Software Integration: DGX systems come pre-installed with Nvidia’s AI software stack, making them ready for immediate deployment.

High Performance: With up to 8 Tensor Core GPUs, DGX systems provide top-tier computational power for AI and HPC tasks.

Scalability: Solutions like the DGX SuperPOD integrate multiple DGX systems to form extensive data center configurations.

Current Offerings

The company currently offers both Hopper-based (DGX H100) and Blackwell-based (DGX B200) systems optimized for AI workloads. Customers can go a step further with solutions like the DGX SuperPOD (with DGX GB200 systems) that integrates 36 liquid-cooled Nvidia GB200 Grace Blackwell Superchips, comprised of 36 Nvidia Grace CPUs and 72 Blackwell GPUs. This monstrous setup includes multiple racks connected through Nvidia Quantum InfiniBand, allowing companies to scale thousands of GB200 Superchips.

Legacy and Evolution

Nvidia has been selling DGX systems for quite some time now — from the DGX Server-1 dating back to 2016 to modern DGX B200-based systems. From the Pascal and Volta generations to the Ampere, Hopper, and Blackwell generations, Nvidia’s enterprise HPC business has pioneered numerous innovations and helped in the birth of its customizable platform, Nvidia HGX.

Nvidia HGX: For Businesses That Need More

Build Your Own Supercomputer

For OEMs looking for custom supercomputing solutions, Nvidia HGX offers the same peak performance as its Hopper and Blackwell-based DGX systems but allows OEMs to tweak it as needed. For instance, customers can modify the CPUs, RAM, storage, and networking configuration as they please. Nvidia HGX is actually the baseboard used in the Nvidia DGX system but adheres to Nvidia’s own standard.

Key Features of HGX

Customization: OEMs have the freedom to modify components such as CPUs, RAM, and storage to suit specific requirements.

Flexibility: HGX allows for a modular approach to building AI and HPC solutions, giving enterprises the ability to scale and adapt.

Performance: Nvidia offers HGX in x4 and x8 GPU configurations, with the latest Blackwell-based baseboards only available in the x8 configuration. An HGX B200 system can deliver up to 144 petaFLOPS of performance.

Applications and Use Cases

HGX is designed for enterprises that need high-performance computing solutions but also want the flexibility to customize their systems. It’s ideal for businesses that require scalable AI infrastructure tailored to specific needs, from deep learning and data analytics to large-scale simulations.

Nvidia DGX vs. HGX: Summary

Simplicity vs. Flexibility

While Nvidia DGX represents Nvidia’s line of standardized, unified, and integrated supercomputing solutions, Nvidia HGX unlocks greater customization and flexibility for OEMs to offer more to enterprise customers.

Rapid Deployment vs. Custom Solutions

With Nvidia DGX, the company leans more into cluster solutions that integrate multiple DGX systems into huge and, in the case of the DGX SuperPOD, multi-million-dollar data center solutions. Nvidia HGX, on the other hand, is another way of selling HPC hardware to OEMs at a greater profit margin.

Unified vs. Modular

Nvidia DGX brings rapid deployment and a seamless, hassle-free setup for bigger enterprises. Nvidia HGX provides modular solutions and greater access to the wider industry.

FAQs

What is the primary difference between Nvidia DGX and HGX?

The primary difference lies in customization. DGX offers a standardized, integrated solution ready for deployment, while HGX provides a customizable platform that OEMs can adapt to specific needs.

Which platform is better for rapid deployment?

Nvidia DGX is better suited for rapid deployment as it comes pre-integrated with Nvidia’s AI software stack and requires minimal setup.

Can HGX be used for scalable AI infrastructure?

Yes, Nvidia HGX is designed for scalable AI infrastructure, offering flexibility to customize and expand as per business requirements.

Are DGX and HGX systems compatible with all AI software?

Both DGX and HGX systems are compatible with Nvidia’s AI software stack, which supports a wide range of AI applications and frameworks.

Final Thoughts

Choosing between Nvidia DGX and HGX ultimately depends on your enterprise’s needs. If you require a turnkey solution with rapid deployment, DGX is your go-to. However, if customization and scalability are your top priorities, HGX offers the flexibility to tailor your HPC system to your specific requirements.

Muhammad Hussnain Facebook | Instagram | Twitter | Linkedin | Youtube

[viperallc]

Exploring the Key Differences: NVIDIA DGX vs NVIDIA HGX Systems

A frequent topic of inquiry we encounter involves understanding the distinctions between the NVIDIA DGX and NVIDIA HGX platforms. Despite the resemblance in their names, these platforms represent distinct approaches NVIDIA employs to market its 8x GPU systems featuring NVLink technology. The shift in NVIDIA’s business strategy was notably evident during the transition from the NVIDIA P100 “Pascal” to the V100 “Volta” generations. This period marked the significant rise in prominence of the HGX model, a trend that has continued through the A100 “Ampere” and H100 “Hopper” generations.

NVIDIA DGX versus NVIDIA HGX What is the Difference

Focusing primarily on the 8x GPU configurations that utilize NVLink, NVIDIA’s product lineup includes the DGX and HGX lines. While there are other models like the 4x GPU Redstone and Redstone Next, the flagship DGX/HGX (Next) series predominantly features 8x GPU platforms with SXM architecture. To understand these systems better, let’s delve into the process of building an 8x GPU system based on the NVIDIA Tesla P100 with SXM2 configuration.

DeepLearning12 Initial Gear Load Out

Each server manufacturer designs and builds a unique baseboard to accommodate GPUs. NVIDIA provides the GPUs in the SXM form factor, which are then integrated into servers by either the server manufacturers themselves or by a third party like STH.

DeepLearning12 Half Heatsinks Installed 800

This task proved to be quite challenging. We encountered an issue with a prominent server manufacturer based in Texas, where they had applied an excessively thick layer of thermal paste on the heatsinks. This resulted in damage to several trays of GPUs, with many experiencing cracks. This experience led us to create one of our initial videos, aptly titled “The Challenges of SXM2 Installation.” The difficulty primarily arose from the stringent torque specifications required during the GPU installation process.

NVIDIA Tesla P100 V V100 Topology

During this development, NVIDIA established a standard for the 8x SXM GPU platform. This standardization incorporated Broadcom PCIe switches, initially for host connectivity, and subsequently expanded to include Infiniband connectivity.

Microsoft HGX 1 Topology

It also added NVSwitch. NVSwitch was a switch for the NVLink fabric that allowed higher performance communication between GPUs. Originally, NVIDIA had the idea that it could take two of these standardized boards and put them together with this larger switch fabric. The impact, though, was that now the NVIDIA GPU-to-GPU communication would occur on NVIDIA NVSwitch silicon and PCIe would have a standardized topology. HGX was born.

NVIDIA HGX 2 Dual GPU Baseboard Layout

Let’s delve into a comparison of the NVIDIA V100 setup in a server from 2020, renowned for its standout color scheme, particularly in the NVIDIA SXM coolers. When contrasting this with the earlier P100 version, an interesting detail emerges. In the Gigabyte server that housed the P100, one could notice that the SXM2 heatsinks were without branding. This marked a significant shift in NVIDIA’s approach. With the advent of the NVSwitch baseboard equipped with SXM3 sockets, NVIDIA upped its game by integrating not just the sockets but also the GPUs and their cooling systems directly. This move represented a notable advancement in their hardware design strategy.

Consequences

The consequences of this development were significant. Server manufacturers now had the option to acquire an 8-GPU module directly from NVIDIA, eliminating the need to apply excessive thermal paste to the GPUs. This change marked the inception of the NVIDIA HGX topology. It allowed server vendors the flexibility to customize the surrounding hardware as they desired. They could select their preferred specifications for RAM, CPUs, storage, and other components, while adhering to the predetermined GPU configuration determined by the NVIDIA HGX baseboard.

Inspur NF5488M5 Nvidia Smi Topology

This was very successful. In the next generation, the NVSwitch heatsinks got larger, the GPUs lost a great paint job, but we got the NVIDIA A100. The codename for this baseboard is “Delta”. Officially, this board was called the NVIDIA HGX.

Inspur NF5488A5 NVIDIA HGX A100 8 GPU Assembly 8x A100 And NVSwitch Heatsinks Side 2

NVIDIA, along with its OEM partners and clients, recognized that increased power could enable the same quantity of GPUs to perform additional tasks. However, this enhancement came with a drawback: higher power consumption led to greater heat generation. This development prompted the introduction of liquid-cooled NVIDIA HGX A100 “Delta” platforms to efficiently manage this heat issue.

Supermicro Liquid Cooling Supermicro

The HGX A100 assembly was initially introduced with its own brand of air cooling systems, distinctively designed by the company.

In the newest “Hopper” series, the cooling systems were upscaled to manage the increased demands of the more powerful GPUs and the enhanced NVSwitch architecture. This upgrade is exemplified in the NVIDIA HGX H100 platform, also known as “Delta Next”.

NVIDIA DGX H100

NVIDIA’s DGX and HGX platforms represent cutting-edge GPU technology, each serving distinct needs in the industry. The DGX series, evolving since the P100 days, integrates HGX baseboards into comprehensive server solutions. Notable examples include the DGX V100 and DGX A100. These systems, crafted by rotating OEMs, offer fixed configurations, ensuring consistent, high-quality performance.

While the DGX H100 sets a high standard, the HGX H100 platform caters to clients seeking customization. It allows OEMs to tailor systems to specific requirements, offering variations in CPU types (including AMD or ARM), Xeon SKU levels, memory, storage, and network interfaces. This flexibility makes HGX ideal for diverse, specialized applications in GPU computing.

Conclusion

NVIDIA’s HGX baseboards streamline the process of integrating 8 GPUs with advanced NVLink and PCIe switched fabric technologies. This innovation allows NVIDIA’s OEM partners to create tailored solutions, giving NVIDIA the flexibility to price HGX boards with higher margins. The HGX platform is primarily focused on providing a robust foundation for custom configurations.

In contrast, NVIDIA’s DGX approach targets the development of high-value AI clusters and their associated ecosystems. The DGX brand, distinct from the DGX Station, represents NVIDIA’s comprehensive systems solution.

Particularly noteworthy are the NVIDIA HGX A100 and HGX H100 models, which have garnered significant attention following their adoption by leading AI initiatives like OpenAI and ChatGPT. These platforms demonstrate the capabilities of the 8x NVIDIA A100 setup in powering advanced AI tools. For those interested in a deeper dive into the various HGX A100 configurations and their role in AI development, exploring the hardware behind ChatGPT offers insightful perspectives on the 8x NVIDIA A100’s power and efficiency.

M.Hussnain Visit us on social media: Facebook | Twitter | LinkedIn | Instagram | YouTube TikTok

[guneyteknoweb]

Elon Musk'ın 100.000 GPU'lu Süper Bilgisayarı İlk Kez Görüntülendi [Video]

Elon Musk’ın montajı 122 gün süren 100.000 GPU’lu süper bilgisayarı xAI Colossus, ilk kez kapılarını açtı. Bir YouTuber, tesisi gezerek görüntülerini paylaştı. Elon Musk’ın yeni projesi xAI Colossus süper bilgisayarı, 100,000 GPU ile donatılmış devasa bir yapay zeka bilgisayarı olarak ilk kez detaylı bir şekilde kameraların önüne çıkarıldı. YouTuber ServeTheHome, süper bilgisayarın…

[pulsaris]

NVIDIA HGX H200 - HBM3e

A NVIDIA anunciou a disponibilização de uma nova plataforma de computação com a designação de HGX H200. Esta nova plataforma é baseada na arquitectura NVIDIA Hopper e utiliza memória HBM3e (um modelo avançado de memória com mais largura de banda e maior capacidade de memória utilizável).

Este é o primeiro modelo a utilizar memória HBM3e, introduzindo outras novidades tecnológicas no processador e na GPU que vão ao encontro das exigências dos mais recentes “modelos de linguagem” complexos e dos projectos de “deep learning”.

A plataforma está optimizada para utilização em Datacenters (centros de dados) e estará comercialmente disponível no segundo semestre de 2024.

Saiba tudo em detalhe na página oficial da NVIDIA localizada em: https://nvidianews.nvidia.com/news/nvidia-supercharges-hopper-the-worlds-leading-ai-computing-platform

______ Direitos de imagem: © NVIDIA Corporation (via NVIDIA Newsroom).

[govindhtech]

Dell PowerEdge XE9680L Cools and Powers Dell AI Factory

When It Comes to Cooling and Powering Your  AI Factory, Think Dell. As part of the Dell AI Factory initiative, the company is thrilled to introduce a variety of new server power and cooling capabilities.

Dell PowerEdge XE9680L Server

As part of the Dell AI Factory, they’re showcasing new server capabilities after a fantastic Dell Technologies World event. These developments, which offer a thorough, scalable, and integrated method of imaplementing AI solutions, have the potential to completely transform the way businesses use artificial intelligence.

These new capabilities, which begin with the PowerEdge XE9680L with support for NVIDIA B200 HGX 8-way NVLink GPUs (graphics processing units), promise unmatched AI performance, power management, and cooling. This offer doubles I/O throughput and supports up to 72 GPUs per rack 107 kW, pushing the envelope of what’s feasible for AI-driven operations.

Integrating AI with Your Data

In order to fully utilise AI, customers must integrate it with their data. However, how can they do this in a more sustainable way? Putting in place state-of-the-art infrastructure that is tailored to meet the demands of AI workloads as effectively as feasible is the solution. Dell PowerEdge servers and software are built with Smart Power and Cooling to assist IT operations make the most of their power and thermal budgets.

Astute Cooling

Effective power management is but one aspect of the problem. Recall that cooling ability is also essential. At the highest workloads, Dell’s rack-scale system, which consists of eight XE9680 H100 servers in a rack with an integrated rear door heat exchanged, runs at 70 kW or less, as we disclosed at Dell Technologies World 2024. In addition to ensuring that component thermal and reliability standards are satisfied, Dell innovates to reduce the amount of power required to maintain cool systems.

Together, these significant hardware advancements including taller server chassis, rack-level integrated cooling, and the growth of liquid cooling, which includes liquid-assisted air cooling, or LAAC improve heat dissipation, maximise airflow, and enable larger compute densities. An effective fan power management technology is one example of how to maximise airflow. It uses an AI-based fuzzy logic controller for closed-loop thermal management, which immediately lowers operating costs.

Constructed to Be Reliable

Dependability and the data centre are clearly at the forefront of Dell’s solution development. All thorough testing and validation procedures, which guarantee that their systems can endure the most demanding situations, are clear examples of this.

A recent study brought attention to problems with data centre overheating, highlighting how crucial reliability is to data centre operations. A Supermicro SYS‑621C-TN12R server failed in high-temperature test situations, however a Dell PowerEdge HS5620 server continued to perform an intense workload without any component warnings or failures.

Announcing AI Factory Rack-Scale Architecture on the Dell PowerEdge XE9680L

Dell announced a factory integrated rack-scale design as well as the liquid-cooled replacement for the Dell PowerEdge XE9680.

The GPU-powered Since the launch of the PowerEdge product line thirty years ago, one of Dell’s fastest-growing products is the PowerEdge XE9680. immediately following the Dell PowerEdge. Dell announced an intriguing new addition to the PowerEdge XE product family as part of their next announcement for cloud service providers and near-edge deployments.

 AI computing has advanced significantly with the Direct Liquid Cooled (DLC) Dell PowerEdge XE9680L with NVIDIA Blackwell Tensor Core GPUs. This server, shown at Dell Technologies World 2024 as part of the Dell AI Factory with NVIDIA, pushes the limits of performance, GPU density per rack, and scalability for AI workloads.

The XE9680L’s clever cooling system and cutting-edge rack-scale architecture are its key components. Why it matters is as follows:

GPU Density per Rack, Low Power Consumption, and Outstanding Efficiency

The most rigorous large language model (LLM) training and large-scale AI inferencing environments where GPU density per rack is crucial are intended for the XE9680L. It provides one of the greatest density x86 server solutions available in the industry for the next-generation NVIDIA HGX B200 with a small 4U form factor.

Efficient DLC smart cooling is utilised by the XE9680L for both CPUs and GPUs. This innovative technique maximises compute power while retaining thermal efficiency, enabling a more rack-dense 4U architecture. The XE9680L offers remarkable performance for training large language models (LLMs) and other AI tasks because it is tailored for the upcoming NVIDIA HGX B200.

More Capability for PCIe 5 Expansion

With its standard 12 x PCIe 5.0 full-height, half-length slots, the XE9680L offers 20% more FHHL PCIe 5.0 density to its clients. This translates to two times the capability for high-speed input/output for the North/South AI fabric, direct storage connectivity for GPUs from Dell PowerScale, and smooth accelerator integration.

The XE9680L’s PCIe capacity enables smooth data flow whether you’re managing data-intensive jobs, implementing deep learning models, or running simulations.

Rack-scale factory integration and a turn-key solution

Dell is dedicated to quality over the XE9680L’s whole lifecycle. Partner components are seamlessly linked with rack-scale factory integration, guaranteeing a dependable and effective deployment procedure.

Bid farewell to deployment difficulties and welcome to faster time-to-value for accelerated AI workloads. From PDU sizing to rack, stack, and cabling, the XE9680L offers a turn-key solution.

With the Dell PowerEdge XE9680L, you can scale up to 72 Blackwell GPUs per 52 RU rack or 64 GPUs per 48 RU rack.

With pre-validated rack infrastructure solutions, increasing power, cooling, and  AI fabric can be done without guesswork.

AI factory solutions on a rack size, factory integrated, and provided with “one call” support and professional deployment services for your data centre or colocation facility floor.

Dell PowerEdge XE9680L

The PowerEdge XE9680L epitomises high-performance computing innovation and efficiency. This server delivers unmatched performance, scalability, and dependability for modern data centres and companies. Let’s explore the PowerEdge XE9680L’s many advantages for computing.

Superior performance and scalability

Enhanced Processing: Advanced processing powers the PowerEdge XE9680L. This server performs well for many applications thanks to the latest Intel Xeon Scalable CPUs. The XE9680L can handle complicated simulations, big databases, and high-volume transactional applications.

Flexibility in Memory and Storage: Flexible memory and storage options make the PowerEdge XE9680L stand out. This server may be customised for your organisation with up to 6TB of DDR4 memory and NVMe,  SSD, and HDD storage. This versatility lets you optimise your server’s performance for any demand, from fast data access to enormous storage.

Strong Security and Management

Complete Security: Today’s digital world demands security. The PowerEdge XE9680L protects data and system integrity with extensive security features. Secure Boot, BIOS Recovery, and TPM 2.0 prevent cyberattacks. Our server’s built-in encryption safeguards your data at rest and in transit, following industry standards.

Advanced Management Tools

Maintaining performance and minimising downtime requires efficient IT infrastructure management. Advanced management features ease administration and boost operating efficiency on the PowerEdge XE9680L. Dell EMC OpenManage offers simple server monitoring, management, and optimisation solutions. With iDRAC9 and Quick Sync 2, you can install, update, and troubleshoot servers remotely, decreasing on-site intervention and speeding response times.

Excellent Reliability and Support

More efficient cooling and power

For optimal performance, high-performance servers need cooling and power control. The PowerEdge XE9680L’s improved cooling solutions dissipate heat efficiently even under intense loads. Airflow is directed precisely to prevent hotspots and maintain stable temperatures with multi-vector cooling. Redundant power supply and sophisticated power management optimise the server’s power efficiency, minimising energy consumption and running expenses.

A proactive support service

The PowerEdge XE9680L has proactive support from Dell to maximise uptime and assure continued operation. Expert technicians, automatic issue identification, and predictive analytics are available 24/7 in ProSupport Plus to prevent and resolve issues before they affect your operations. This proactive assistance reduces disruptions and improves IT infrastructure stability, letting you focus on your core business.

Innovation in Modern Data Centre Design Scalable Architecture

The PowerEdge XE9680L’s scalable architecture meets modern data centre needs. You can extend your infrastructure as your business grows with its modular architecture and easy extension and customisation. Whether you need more storage, processing power, or new technologies, the XE9680L can adapt easily.

Ideal for virtualisation and clouds

Cloud computing and virtualisation are essential to modern IT strategies. Virtualisation support and cloud platform integration make the PowerEdge XE9680L ideal for these environments. VMware, Microsoft Hyper-V, and OpenStack interoperability lets you maximise resource utilisation and operational efficiency with your visualised infrastructure.

Conclusion

Finally, the PowerEdge XE9680L is a powerful server with flexible memory and storage, strong security, and easy management. Modern data centres and organisations looking to improve their IT infrastructure will love its innovative design, high reliability, and proactive support. The PowerEdge XE9680L gives your company the tools to develop, innovate, and succeed in a digital environment.

Read more on govindhtech.com

[dr-iphone]

WEKA 獲 NVIDIA 雲端合作夥伴認證，提升 AI 資料儲存效能

AI 資料平台公司 WEKA 宣布資料平台正式通過 NVIDIA 雲端合作夥伴網路的高效能資料儲存解決方案認證。這項認證讓 WEKA 成為 NVIDIA 雲端合作夥伴的強力支持者，尤其針對搭載 NVIDIA HGX H100 系統的 AI 環境，提供極具效能、可擴展性與簡化操作的資料儲存方案。 Continue reading WEKA 獲 NVIDIA 雲端合作夥伴認證，提升 AI 資料儲存效能

[hamzaaslam]

ASUS Announces ESC N8-E11 AI Server with NVIDIA HGX H200

New server offering solidifies ASUS leadership in AI, with first deal secured SINGAPORE – Media OutReach Newswire – 3 September 2024 – ASUS today announced the latest marvel in the groundbreaking lineup of ASUS AI servers ― ESC N8-E11, featuring the intensely powerful NVIDIA® HGX™ H200 platform. With this AI titan, ASUS has secured its first industry deal, showcasing the exceptional performance,…

[7ooo-ru]

Gigabyte представила ИИ-серверы с ускорителями NVIDIA H200 и процессорами AMD и Intel

Компания Gigabyte анонсировала HGX-серверы G593-SD1-AAX3 и G593-ZD1-AAX3, предназначенные для задач ИИ и НРС. Устройства, выполненные в форм-факторе 5U, включают до восьми ускорителей NVIDIA H200. При этом используется воздушное охлаждение. Источник изображений: Gigabyte

Подробнее https://7ooo.ru/group/2024/08/19/963-gigabyte-predstavila-ii-servery-s-uskoritelyaminvidia-h200-i-processoramiamd-i-intel-grss-333941177.html

[hardwareholic]

GIGABYTE Memperkenalkan Server Komputasi yang Dipercepat dengan Perolehan Bandwidth Memori yang Signifikan Menggunakan Platform NVIDIA HGX™ H200

Giga Computing, anak perusahaan GIGABYTE dan pemimpin industri dalam server AI generatif dan teknologi pendinginan canggih, hari ini menambahkan dua server baseboard 8-GPU baru ke seri GIGABYTE G593 yang mendukung NVIDIA HGX™ H200, platform memori GPU yang ideal untuk kumpulan data AI besar, serta simulasi ilmiah dan beban kerja intensif memori lainnya. Seri G593 untuk Scale-up Computing di AI &…

[levysoft]

In un post, NVIDIA afferma che una singola GPU NVIDIA H200 Tensor core ha generato circa 3000 token al secondo, abbastanza per servire simultaneamente 300mila utenti - in un test iniziale usando Llama 3 con 70B di parametri. "Questo significa che un singolo server NVIDIA HGX con otto GPU H200 può fornire 24.000 token al secondo e supportare oltre 2400 utenti nello stesso momento". Per i dispositivi edge, la versione con 8 miliardi di parametri di Llama 3 ha generato fino a 40 token al secondo su Jetson AGX Orin e 15 token al secondo su Jetson Orin Nano.

[exeton]

NVIDIA HGX AI Supercomputer Now Available at Exeton - Experience Next-Gen AI Computing

Exeton proudly announces a significant advancement in computing technology with the availability of the NVIDIA HGX AI Supercomputer. Tailored for high-caliber AI research and high-performance computing (HPC) tasks, the NVIDIA HGX is your gateway to achieving unparalleled computational speeds and efficiency.

The NVIDIA HGX AI platform is offered in several powerful configurations. Customers can choose from single baseboards with four H200 or H100 GPUs, or opt for the more expansive eight-GPU configurations which include combinations of H200, H100, B200, or B100 models. These setups are engineered to handle the most complex and demanding tasks across various industries including AI development, deep learning, and scientific research.

Central to the NVIDIA HGX B200 and B100 models are the innovative Blackwell Tensor Core GPUs. These are seamlessly integrated with high-speed interconnects to propel your data center into the next era of accelerated computing. Offering up to 15X more inference performance than its predecessors, the Blackwell-based HGX systems are ideal for running complex generative AI models, performing advanced data analytics, and managing intensive HPC operations.

The HGX H200 model takes performance to the next level, combining H200 Tensor Core GPUs with state-of-the-art interconnects for superior scalability and security. Capable of delivering up to 32 petaFLOPS of computational power, the HGX H200 sets a new benchmark as the world’s most potent accelerated scale-up server platform for AI and high-performance computing.

Exeton is excited to offer this revolutionary tool that redefines what is possible in AI and HPC. The NVIDIA HGX AI Supercomputer is not just a piece of technology; it is your partner in pushing the boundaries of what is computationally possible. Visit Exeton today and choose the NVIDIA HGX model that will drive your ambitions to new computational heights.

[viperallc]

Exploring the Key Differences: NVIDIA DGX vs NVIDIA HGX Systems

A frequent topic of inquiry we encounter involves understanding the distinctions between the NVIDIA DGX and NVIDIA HGX platforms. Despite the resemblance in their names, these platforms represent distinct approaches NVIDIA employs to market its 8x GPU systems featuring NVLink technology. The shift in NVIDIA’s business strategy was notably evident during the transition from the NVIDIA P100 “Pascal” to the V100 “Volta” generations. This period marked the significant rise in prominence of the HGX model, a trend that has continued through the A100 “Ampere” and H100 “Hopper” generations.

NVIDIA DGX versus NVIDIA HGX What is the Difference

Focusing primarily on the 8x GPU configurations that utilize NVLink, NVIDIA’s product lineup includes the DGX and HGX lines. While there are other models like the 4x GPU Redstone and Redstone Next, the flagship DGX/HGX (Next) series predominantly features 8x GPU platforms with SXM architecture. To understand these systems better, let’s delve into the process of building an 8x GPU system based on the NVIDIA Tesla P100 with SXM2 configuration.

DeepLearning12 Initial Gear Load Out

Each server manufacturer designs and builds a unique baseboard to accommodate GPUs. NVIDIA provides the GPUs in the SXM form factor, which are then integrated into servers by either the server manufacturers themselves or by a third party like STH.

DeepLearning12 Half Heatsinks Installed 800

This task proved to be quite challenging. We encountered an issue with a prominent server manufacturer based in Texas, where they had applied an excessively thick layer of thermal paste on the heatsinks. This resulted in damage to several trays of GPUs, with many experiencing cracks. This experience led us to create one of our initial videos, aptly titled “The Challenges of SXM2 Installation.” The difficulty primarily arose from the stringent torque specifications required during the GPU installation process.

NVIDIA Tesla P100 V V100 Topology

During this development, NVIDIA established a standard for the 8x SXM GPU platform. This standardization incorporated Broadcom PCIe switches, initially for host connectivity, and subsequently expanded to include Infiniband connectivity.

Microsoft HGX 1 Topology

It also added NVSwitch. NVSwitch was a switch for the NVLink fabric that allowed higher performance communication between GPUs. Originally, NVIDIA had the idea that it could take two of these standardized boards and put them together with this larger switch fabric. The impact, though, was that now the NVIDIA GPU-to-GPU communication would occur on NVIDIA NVSwitch silicon and PCIe would have a standardized topology. HGX was born.

NVIDIA HGX 2 Dual GPU Baseboard Layout

Let’s delve into a comparison of the NVIDIA V100 setup in a server from 2020, renowned for its standout color scheme, particularly in the NVIDIA SXM coolers. When contrasting this with the earlier P100 version, an interesting detail emerges. In the Gigabyte server that housed the P100, one could notice that the SXM2 heatsinks were without branding. This marked a significant shift in NVIDIA’s approach. With the advent of the NVSwitch baseboard equipped with SXM3 sockets, NVIDIA upped its game by integrating not just the sockets but also the GPUs and their cooling systems directly. This move represented a notable advancement in their hardware design strategy.

Consequences

The consequences of this development were significant. Server manufacturers now had the option to acquire an 8-GPU module directly from NVIDIA, eliminating the need to apply excessive thermal paste to the GPUs. This change marked the inception of the NVIDIA HGX topology. It allowed server vendors the flexibility to customize the surrounding hardware as they desired. They could select their preferred specifications for RAM, CPUs, storage, and other components, while adhering to the predetermined GPU configuration determined by the NVIDIA HGX baseboard.

Inspur NF5488M5 Nvidia Smi Topology

This was very successful. In the next generation, the NVSwitch heatsinks got larger, the GPUs lost a great paint job, but we got the NVIDIA A100.

The codename for this baseboard is “Delta”.

Officially, this board was called the NVIDIA HGX.

Inspur NF5488A5 NVIDIA HGX A100 8 GPU Assembly 8x A100 And NVSwitch Heatsinks Side 2

NVIDIA, along with its OEM partners and clients, recognized that increased power could enable the same quantity of GPUs to perform additional tasks. However, this enhancement came with a drawback: higher power consumption led to greater heat generation. This development prompted the introduction of liquid-cooled NVIDIA HGX A100 “Delta” platforms to efficiently manage this heat issue.

Supermicro Liquid Cooling Supermicro

The HGX A100 assembly was initially introduced with its own brand of air cooling systems, distinctively designed by the company.

In the newest “Hopper” series, the cooling systems were upscaled to manage the increased demands of the more powerful GPUs and the enhanced NVSwitch architecture. This upgrade is exemplified in the NVIDIA HGX H100 platform, also known as “Delta Next”.

NVIDIA DGX H100

NVIDIA’s DGX and HGX platforms represent cutting-edge GPU technology, each serving distinct needs in the industry. The DGX series, evolving since the P100 days, integrates HGX baseboards into comprehensive server solutions. Notable examples include the DGX V100 and DGX A100. These systems, crafted by rotating OEMs, offer fixed configurations, ensuring consistent, high-quality performance.

While the DGX H100 sets a high standard, the HGX H100 platform caters to clients seeking customization. It allows OEMs to tailor systems to specific requirements, offering variations in CPU types (including AMD or ARM), Xeon SKU levels, memory, storage, and network interfaces. This flexibility makes HGX ideal for diverse, specialized applications in GPU computing.

Conclusion

NVIDIA’s HGX baseboards streamline the process of integrating 8 GPUs with advanced NVLink and PCIe switched fabric technologies. This innovation allows NVIDIA’s OEM partners to create tailored solutions, giving NVIDIA the flexibility to price HGX boards with higher margins. The HGX platform is primarily focused on providing a robust foundation for custom configurations.

In contrast, NVIDIA’s DGX approach targets the development of high-value AI clusters and their associated ecosystems. The DGX brand, distinct from the DGX Station, represents NVIDIA’s comprehensive systems solution.

Particularly noteworthy are the NVIDIA HGX A100 and HGX H100 models, which have garnered significant attention following their adoption by leading AI initiatives like OpenAI and ChatGPT. These platforms demonstrate the capabilities of the 8x NVIDIA A100 setup in powering advanced AI tools. For those interested in a deeper dive into the various HGX A100 configurations and their role in AI development, exploring the hardware behind ChatGPT offers insightful perspectives on the 8x NVIDIA A100’s power and efficiency.

[rohitpalan]

Navigating the Growth Trajectory: Graphics Processing Unit Market Set to Exceed US$ 70.9 Billion in 2024

The graphics processing unit market is estimated to be worth US$ 70.9 billion in 2024 and is projected to be valued at US$ 1,159.3 billion in 2034. Between 2024 and 2034, the industry is expected to register a growth rate of 32.2%.

The demand for more powerful GPUs surged as gaming enthusiasts sought enhanced graphical experiences, higher resolutions, and smoother frame rates, driving market demand during the forecast period. The rising demand for GPUs in data centers for high-performance computing, cloud services, and AI-related tasks further boosted market growth.

With the increasing digitalization in various industries and sectors, the need for efficient and powerful computing solutions, including GPUs, continues to rise. The rising new applications such as AR/VR, automotive computing, and edge computing rely on GPUs, broadening their use cases and increasing market demand.

Get Sample Copy of this Report at: https://www.futuremarketinsights.com/reports/sample/rep-gb-18664

Data centers rely on GPUs for parallel processing tasks like AI inference, data analytics, and scientific simulations, contributing significantly to market growth. GPUs excel in parallel processing, making them pivotal for AI and machine learning tasks. As these technologies become integral across industries, the demand for GPUs grows.

The automotive industry increasingly integrates GPUs for infotainment systems, advanced driver-assistance systems (ADAS), and autonomous vehicle development, boosting demand for specialized GPUs tailored for these applications.

The growing regulatory compliance promoting energy efficiency and environmental sustainability might drive the development of more energy-efficient GPUs, influencing market demand.

Key Takeaways

From 2019 to 2023, the graphics processing unit market was valued at a CAGR of 29.7%

Based on type, the integrated GPUs segment is expected to account for a share of 35% in 2024.

Global graphics processing unit demand in China is predicted to account for a CAGR of 32.8% in 2024.

In the United States, the graphics processing unit industry is expected to account for a CAGR of 30.1% in 2024.

Germany is projected to expand by a value CAGR of 31.7% between 2024 and 2034.

Graphics processing unit market in Japan is anticipated to record a CAGR of 33.5% in 2024.

“The increasing growth of edge computing and IoT applications and strong gaming culture are anticipated to drive the market growth during the forecast period.” opines Sudip Saha, managing director at Future Market Insights (FMI) analyst.

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Competitive Landscape

Companies within the market are actively investing in research and development aimed at crafting cutting-edge graphic processing units (GPUs). They focus on creating more robust, efficient, cost-effective solutions to meet evolving demands. Leading players in the graphics processing unit market are

Intel Corporation

Advanced Micro Devices Inc.

Nvidia Corporation

Imagination Technologies Group

Samsung Electronics Co. Ltd.

Arm Limted (soft Bank Group)

EVGA Corporation

SAPPHIRE Technology Limited

Qualcomm Technologies Inc.

Some of the developments in the graphics processing unit market are

In November 2023, NVIDIA elevated its AI computing platform to unprecedented levels by unveiling the cutting-edge NVIDIA HGX H200. Harnessing the formidable power of the NVIDIA Hopper architecture, this innovation highlights the exceptional prowess of the NVIDIA H200 Tensor Core GPU. Tailored to efficiently process vast datasets, this GPU emerges as a vital resource for high-performance computing workloads, particularly those focused on generative AI assignments.

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Graphics Processing Unit Market Key Segments

By Type:

Dedicated

Integrated

Hybrid

By Application:

Computer

Tablet

Smartphone

Gaming Console

Television

Others

By Region:

North America

Latin America

Western Europe

Eastern Europe

South Asia and Pacific

East Asia

Middle East and Africa

Author:

Sudip Saha is the managing director and co-founder at Future Market Insights, an award-winning market research and consulting firm. Sudip is committed to shaping the market research industry with credible solutions and constantly makes a buzz in the media with his thought leadership. His vast experience in market research and project management a consumer electronics will likely remain the leading end-use sector cross verticals in APAC, EMEA, and the Americas reflects his growth-oriented approach to clients.

He is a strong believer and proponent of innovation-based solutions, emphasizing customized solutions to meet one client’s requirements at a time. His foresightedness and visionary approach recently got him recognized as the ‘Global Icon in Business Consulting’ at the ET Inspiring Leaders Awards 2022.

[topreviewin]

Apple is expected to exercise loads of billion on hardware to enhance its artificial intelligence style in 2024, in accordance with speculation from Apple analyst Ming-Chi Kuo. Kuo expects Apple to exercise "at the least" $620 million on servers in 2023 and $4.75 billion on servers in 2024. Apple may maybe maybe contain between 2,000 and 3,000 servers this yr, and as much as twenty,000 next yr. Kuo thinks that Apple is purchasing servers outfitted with Nvidia's HGX H100 8-GPU for generative AI coaching, with the corporate planning to upgrade to B100 next yr. Nvidia calls its H100 an AI supercomputing platform, and each is priced at around $250,000. Kuo looks guessing at Apple's purchasing plans here, and he says that he expects Apple will exercise AI servers it's a long way purchasing and putting in itself to put together astronomical language objects in site of digital internet webhosting from assorted cloud provider companies for improved security and privateness. He does boom that Apple may maybe maybe way its enjoy server chips to assign on server costs, nonetheless he has considered no evidence that Apple is doing that right this moment. While Apple looks making a prime investment into AI, Apple's server purchasing will drop at the wait on of assorted corporations love Meta and Microsoft. Apple may maybe also procure to put money into labor costs, infrastructure, and additional, and Kuo suggests that Apple must exercise loads of billion bucks each yr to procure a broad gamble of catching up with competitors. Kuo claims that he's "in actuality concerned" relating to the vogue forward for Apple's generative AI enterprise if Apple spends only one billion bucks a yr as advised by Bloomberg's Designate Gurman. Over the weekend, Gurman mentioned that Apple is heading within the reliable route to exercise $1 billion per yr on its AI efforts. Gurman says that Apple is engaged on a brand novel, smarter model of Siri and is aiming to combine AI into many Apple apps. Current Reports iOS 17.1 On hand Subsequent Week With These 8 Recent Components for iPhones iOS 17.1 is expected to be released by Tuesday, October 24 following weeks of beta testing. The tool replace entails loads of novel parts and adjustments for iPhones connected to Apple Music, AirDrop, StandBy mode, and additional. Beneath, we now procure got recapped eight novel parts and adjustments coming to the iPhone with iOS 17.1. When the tool replace is on hand, users would maybe be ready to set up it by... Gurman: Apple to Train Recent Macs This Month Apple is planning a Mac-targeted product delivery, likely together with the announcement of a refreshed 24-hunch iMac, for the head of this month, Bloomberg's Designate Gurman stories. Subscribe to the MacRumors YouTube channel for extra movies. Within the most traditional edition of his "Energy On" e-newsletter, Gurman mentioned that Apple is "planning a Mac-centered product delivery around the head of this month" that would watch... iOS 17.1 At possibility of Initiate Tomorrow Apple's iOS 17.1, iPadOS 17.1, macOS Sonoma 14.1, tvOS 17.1, watchOS 10.1, and HomePod Software 17.1 updates are expected to be released to the general public the following day following loads of weeks of beta testing. We're looking ahead to the tool to head reside at 10:00 a.m. Pacific Time, which is when Apple assuredly releases updates. Remaining week, Apple seeded liberate candidates (RCs) for all of the upcoming... Fingers-On With the $1,700 'OnePlus Originate' Foldable Smartphone Chinese smartphone company OnePlus this week introduced its first foldable smartphone, the OnePlus Originate. OnePlus joins loads of assorted producers that procure come out with foldable smartphones, together with Samsung, Google, and Xiaomi. We picked up the OnePlus Originate to look at how it compares to Apple's most traditional flagship, the iPhone 15 Professional Max. Subscribe to the MacRumors YouTube channel for extra movies. ... Unreleased

HomePod With LCD Display Allegedly Proven in Shots [Update] Change: Kosutami has since published that the LcdUTest app shown on the HomePod's elevated veil panel is faked, nonetheless the HomePod within the checklist is no longer. Apple is rumored to be increasing a brand novel HomePod with a veil, and novel photos shared on-line allegedly give us a first gape of the novel dapper speaker. The above checklist seems to veil a HomePod similar in size to the second-technology... Top Reports: Recent USB-C Apple Pencil, iPad and iMac Rumors, and Extra After a flurry of rumors suggesting we would watch some novel iPad objects this week, a brand novel Apple Pencil grew to turn into out to the truth is be what became within the playing cards. The novel Apple Pencil costs by technique of USB-C, launches early next month, and is the most payment-efficient mannequin in a lineup that now entails three assorted Apple Pencils. This week saw some extra rumors about future iPad and iMac objects, while we're... Apple Rumored to Adjust to ChatGPT With Generative AI Components on iPhone as Soon as iOS 18 Apple plans to launch imposing generative AI technology on the iPhone and iPad in late 2024 at the earliest, in accordance with Jeff Pu, an analyst who covers Apple's provide chain for Hong Kong-basically based investment firm Haitong Global Securities. In a study reward on Wednesday, Pu mentioned his provide chain assessments imply that Apple is at possibility of create about a hundred AI servers in 2023, and... Bloomberg: Apple At possibility of Care for Mac Initiate Occasion on October 30 or 31 Apple is likely planning a Mac-connected delivery tournament this month that can happen on either Monday, October 30 or Tuesday, October 31, in accordance with Bloomberg's Designate Gurman. Subscribe to the MacRumors YouTube channel for extra movies. The effectively-connected reporter's prediction is in accordance to records purchased from sources with obvious records of Apple's plans, as effectively because the fact that some...

[govindhtech]

Agentic RAG On Dell & NVIDIA Changes AI-Driven Data Access

Agentic RAG Changes AI Data Access with Dell & NVIDIA

The secret to successfully implementing and utilizing AI in today’s corporate environment is comprehending the use cases within the company and determining the most effective and frequently quickest AI-ready strategies that produce outcomes fast. There is also a great need for high-quality data and effective retrieval techniques like RAG retrieval augmented generation. The value of AI for businesses is further accelerated at SC24 by fresh innovation at the Dell AI Factory with NVIDIA, which also gets them ready for the future.

AI Applications Place New Demands

GenAI applications are growing quickly and proliferating throughout the company as businesses gain confidence in the results of applying AI to their departmental use cases. The pressure on the AI infrastructure increases as the use of larger, foundational LLMs increases and as more use cases with multi-modal outcomes are chosen.

RAG’s capacity to facilitate richer decision-making based on an organization’s own data while lowering hallucinations has also led to a notable increase in interest. RAG is particularly helpful for digital assistants and chatbots with contextual data, and it can be easily expanded throughout the company to knowledge workers. However, RAG’s potential might still be limited by inadequate data, a lack of multiple sourcing, and confusing prompts, particularly for large data-driven businesses.

It will be crucial to provide IT managers with a growth strategy, support for new workloads at scale, a consistent approach to AI infrastructure, and innovative methods for turning massive data sets into useful information.

Raising the AI Performance bar

The performance for AI applications is provided by the Dell AI Factory with NVIDIA, giving clients a simplified way to deploy AI using a scalable, consistent, and outcome-focused methodology. Dell is now unveiling new NVIDIA accelerated compute platforms that have been added to Dell AI Factory with NVIDIA. These platforms offer acceleration across a wide range of enterprise applications, further efficiency for inferencing, and performance for developing AI applications.

The NVIDIA HGX H200 and NVIDIA H100 NVL platforms, which are supercharging data centers, offer state-of-the-art technology with enormous processing power and enhanced energy efficiency for genAI and HPC applications. Customers who have already implemented the Dell AI Factory with NVIDIA may quickly grow their footprint with the same excellent foundations, direction, and support to expedite their AI projects with these additions for PowerEdge XE9680 and rack servers. By the end of the year, these combinations with NVIDIA HGX H200 and H100 NVL should be available.

Deliver Informed Decisions, Faster

RAG already provides enterprises with genuine intelligence and increases productivity. Expanding RAG’s reach throughout the company, however, may make deployment more difficult and affect quick response times. In order to provide a variety of outputs, or multi-modal outcomes, large, data-driven companies, such as healthcare and financial institutions, also require access to many data kinds.

Innovative approaches to managing these enormous data collections are provided by agentic RAG. Within the RAG framework, it automates analysis, processing, and reasoning through the use of AI agents. With this method, users may easily combine structured and unstructured data, providing trustworthy, contextually relevant insights in real time.

Organizations in a variety of industries can gain from a substantial advancement in AI-driven information retrieval and processing with Agentic RAG on the Dell AI Factory with NVIDIA. Using the healthcare industry as an example, the agentic RAG design demonstrates how businesses can overcome the difficulties posed by fragmented data (accessing both structured and unstructured data, including imaging files and medical notes, while adhering to HIPAA and other regulations). The complete solution, which is based on the NVIDIA and Dell AI Factory platforms, has the following features:

PowerEdge servers from Dell that use NVIDIA L40S GPUs

Storage from Dell PowerScale

Spectrum-X Ethernet networking from NVIDIA

Platform for NVIDIA AI Enterprise software

Together with the NVIDIA Llama-3.1-8b-instruct LLM NIM microservice, NVIDIA NeMo embeds and reranks NVIDIA NIM microservices.

The recently revealed NVIDIA Enterprise Reference Architecture for NVIDIA L40S GPUs serves as the foundation for the solution, which allows businesses constructing AI factories to power the upcoming generation of generative AI solutions cut down on complexity, time, and expense.

A thorough beginning strategy for enterprises to modify and implement their own Agentic RAG and raise the standard of value delivery is provided by the full integration of these components.

Readying for the Next Era of AI

As employees, developers, and companies start to use AI to generate value, new applications and uses for the technology are released on a daily basis. It can be intimidating to be ready for a large-scale adoption, but any company can change its operations with the correct strategy, partner, and vision.

The Dell AI factory with NVIDIA offers a scalable architecture that can adapt to an organization’s changing needs, from state-of-the-art AI operations to enormous data set ingestion and high-quality results.

The first and only end-to-end enterprise AI solution in the industry, the Dell AI Factory with NVIDIA, aims to accelerate the adoption of AI by providing integrated Dell and NVIDIA capabilities to speed up your AI-powered use cases, integrate your data and workflows, and let you create your own AI journey for scalable, repeatable results.

What is Agentic Rag?

An AI framework called Agentic RAG employs intelligent agents to do tasks beyond creating and retrieving information. It is a development of the classic Retrieval-Augmented Generation (RAG) method, which blends generative and retrieval-based models.

Agentic RAG uses AI agents to:

Data analysis: Based on real-time input, agentic RAG systems are able to evaluate data, improve replies, and make necessary adjustments.

Make choices: Agentic RAG systems are capable of making choices on their own.

Dividing complicated tasks into smaller ones and allocating distinct agents to each component is possible with agentic RAG systems.

Employ external tools: To complete tasks, agentic RAG systems can make use of any tool or API.

Recall what has transpired: Because agentic RAG systems contain memory, like as chat history, they are aware of past events and know what to do next.

For managing intricate questions and adjusting to changing information environments, agentic RAG is helpful. Applications for it are numerous and include:

Management of knowledge

Large businesses can benefit from agentic RAG systems’ ability to generate summaries, optimize searches, and obtain pertinent data.

Research

Researchers can generate analyses, synthesize findings, and access pertinent material with the use of agentic RAG systems.

Read more on govindhtech.com

[h1p3rn0v4]

Team Green anunció la nueva plataforma durante la conferencia Supercomputing 2023 en Denver, Colorado. Basado en la arquitectura Hopper, se espera que el H200 ofrezca casi el doble de velocidad de inferencia que el H100 en Llama 2, que es un modelo de lenguaje grande (LLM) con 70 mil millones de parámetros. El H200 también proporciona alrededor de 1,6 veces la velocidad de inferencia cuando se utiliza el modelo GPT-3, que tiene 175 mil millones de parámetros.

Nvidia dice que el H200 está diseñado para ser compatible con los mismos sistemas que admiten la GPU H100. Dicho esto, el H200 estará disponible en varios factores de forma, como placas de servidor HGX H200 con configuraciones de cuatro u ocho vías o como un superchip Grace Hopper GH200 donde se combinará con una potente CPU basada en Arm de 72 núcleos en el mismo junta. El GH200 permitirá hasta 1,1 terabytes de memoria agregada de alto ancho de banda y 32 petaflops de rendimiento FP8 para aplicaciones de aprendizaje profundo.

Microsoft Azure, Google Cloud, Amazon Web Services y Oracle Cloud Infrastructure serán los primeros proveedores de nube en ofrecer acceso a instancias basadas en H200 a partir del segundo trimestre de 2024.